The invention provides an intelligent
greenhouse photosynthetic optimization method and
system, and the method comprises the steps: carrying out the real-time monitoring of a multi-source sensor network in a
greenhouse, and carrying out the deployment of the sensor network in a high-density state; the data are analyzed and processed by using a
deep learning technology, an optimal illumination strategy for different crops is generated, and a model is trained by using a large amount of
labeled data, so that the optimal illumination strategy required by a specific
crop under a given condition can be predicted. According to the invention, accurate monitoring and regulation of environmental parameters in the
greenhouse are realized through an accurate
algorithm, so that optimal illumination conditions are provided for different crops to improve the
growth speed and yield of the crops, the
moisture content required by the crops is accurately calculated through an AI
algorithm, and a micro-drop
irrigation technology is used to directly act on a root area, so that the waste of
moisture is reduced, and the
crop quality is improved. And according to the growth stage of the
crop and the
soil test result, the AI
algorithm can accurately calculate the
mass of the required nutrients, and excessive or insufficient fertilization is avoided.